Visual Analytics on Network Forgetting for Task‐Incremental Learning
Task‐incremental learning (Task‐IL) aims to enable an intelligent agent to continuously accumulate knowledge from new learning tasks without catastrophically forgetting what it has learned in the past. It has drawn increasing attention in recent years, with many algorithms being proposed to mitigate...
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| Veröffentlicht in: | Computer graphics forum Jg. 42; H. 3; S. 437 - 448 |
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| Format: | Journal Article |
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Blackwell Publishing Ltd
01.06.2023
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Task‐incremental learning (Task‐IL) aims to enable an intelligent agent to continuously accumulate knowledge from new learning tasks without catastrophically forgetting what it has learned in the past. It has drawn increasing attention in recent years, with many algorithms being proposed to mitigate neural network forgetting. However, none of the existing strategies is able to completely eliminate the issues. Moreover, explaining and fully understanding what knowledge and how it is being forgotten during the incremental learning process still remains under‐explored. In this paper, we propose KnowledgeDrift, a visual analytics framework, to interpret the network forgetting with three objectives: (1) to identify when the network fails to memorize the past knowledge, (2) to visualize what information has been forgotten, and (3) to diagnose how knowledge attained in the new model interferes with the one learned in the past. Our analytical framework first identifies the occurrence of forgetting by tracking the task performance under the incremental learning process and then provides in‐depth inspections of drifted information via various levels of data granularity. KnowledgeDrift allows analysts and model developers to enhance their understanding of network forgetting and compare the performance of different incremental learning algorithms. Three case studies are conducted in the paper to further provide insights and guidance for users to effectively diagnose catastrophic forgetting over time. |
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| AbstractList | Task‐incremental learning (Task‐IL) aims to enable an intelligent agent to continuously accumulate knowledge from new learning tasks without catastrophically forgetting what it has learned in the past. It has drawn increasing attention in recent years, with many algorithms being proposed to mitigate neural network forgetting. However, none of the existing strategies is able to completely eliminate the issues. Moreover, explaining and fully understanding what knowledge and how it is being forgotten during the incremental learning process still remains under‐explored. In this paper, we propose KnowledgeDrift, a visual analytics framework, to interpret the network forgetting with three objectives: (1) to identify when the network fails to memorize the past knowledge, (2) to visualize what information has been forgotten, and (3) to diagnose how knowledge attained in the new model interferes with the one learned in the past. Our analytical framework first identifies the occurrence of forgetting by tracking the task performance under the incremental learning process and then provides in‐depth inspections of drifted information via various levels of data granularity. KnowledgeDrift allows analysts and model developers to enhance their understanding of network forgetting and compare the performance of different incremental learning algorithms. Three case studies are conducted in the paper to further provide insights and guidance for users to effectively diagnose catastrophic forgetting over time. |
| Author | Chao, Wei‐Lun Xu, Jiayi Li, Ziwei Shen, Han‐Wei |
| Author_xml | – sequence: 1 givenname: Ziwei surname: Li fullname: Li, Ziwei organization: The Ohio State University – sequence: 2 givenname: Jiayi surname: Xu fullname: Xu, Jiayi organization: The Ohio State University – sequence: 3 givenname: Wei‐Lun surname: Chao fullname: Chao, Wei‐Lun organization: The Ohio State University – sequence: 4 givenname: Han‐Wei surname: Shen fullname: Shen, Han‐Wei organization: The Ohio State University |
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| Cites_doi | 10.32614/CRAN.package.uwot 10.1073/pnas.1611835114 10.1080/095400900116177 10.1016/j.neunet.2019.01.012 10.1109/TVCG.2020.2973258 10.1007/s11263-015-0816-y 10.1109/CVPR.2016.90 10.1109/VIS47514.2020.00065 10.1145/2702123.2702509 10.1109/VAST.2018.8802509 10.1609/aaai.v34i09.7079 10.1109/TVCG.2016.2598838 10.1109/TPAMI.2017.2773081 10.1145/3027063.3053103 10.1007/978-1-4615-5529-2_11 10.1007/978-3-030-01252-6_33 10.1609/aaai.v32i1.11595 10.1109/TVCG.2022.3209347 10.3389/fpsyg.2013.00504 10.1109/ICCV.2019.00653 10.1016/S0764-4469(97)82472-9 10.1109/ICCV.2017.322 10.1109/TVCG.2020.3030461 10.1109/TVCG.2018.2864504 |
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| SubjectTerms | Algorithms CCS Concepts Cognitive tasks Computing methodologies → Visual analytics Intelligent agents Learning Machine learning Mathematical analysis Neural networks Theory of computation → Continual learning |
| Title | Visual Analytics on Network Forgetting for Task‐Incremental Learning |
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